Digital Auscultation Analysis for Heart Murmur Detection

Abstract

This work presents a comparison of different approaches for the detection of murmurs from phonocardiographic signals. Taking into account the variability of the phonocardiographic signals induced by valve disorders, three families of features were analyzed: (a) time-varying & time-frequency features; (b) perceptual; and (c) fractal features. With the aim of improving the performance of the system, the accuracy of the system was tested using several combinations of the aforementioned families of parameters. In the second stage, the main components extracted from each family were combined together with the goal of improving the accuracy of the system. The contribution of each family of features extracted was evaluated by means of a simple k-nearest neighbors classifier, showing that fractal features provide the best accuracy (97.17%), followed by time-varying & time-frequency (95.28%), and perceptual features (88.7%). However, an accuracy around 94% can be reached just by using the two main features of the fractal family; therefore, considering the difficulties related to the automatic intrabeat segmentation needed for spectral and perceptual features, this scheme becomes an interesting alternative. The conclusion is that fractal type features were the most robust family of parameters (in the sense of accuracy vs. computational load) for the automatic detection of murmurs. This work was carried out using a database that contains 164 phonocardiographic recordings (81 normal and 83 records with murmurs). The database was segmented to extract 360 representative individual beats (180 per class).

Feature Extraction From Parametric Time–Frequency Representations for Heart Murmur Detection

“…The results obtained using the methods in DelgadoTrejos et al 8 and Quiceno-Manrique et al 18 were carried out using a subset of the database used in this study that did not contain diastolic murmurs. …”

“…Moreover, the results were compared with other assumed baseline methods in the literature. 8,23 The rest of this article is organized as follows: first, the time-varying representation implemented is introduced, followed by a brief description of the TVAR parameter estimation method used, computed by minimizing the mean-square error. Then, the methodology for feature extraction is described in detail (based on linear decomposition and partition schemes).…”

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

“…This paper presents a complementary study of the methodology proposed in a previous work, 16 in witch t-f static features were compared with nonlinear and perceptual features using the same database of PCG signals. A major motivation in this work is to generate a set of time-varying feature contours, based on nonparametric TFR, and capable of representing the dynamics of the PCG signal integrating the t-f information present in the PCG signal.…”

Exploratory Study of the Cardiac Dynamic Trajectory in the Embedding Space

“…Using a K-nearest neighbor's classifier they observed that fractal features provide the best accuracy (97, 17%) followed by spectral (95, 28%) and perceptual features (88,7%). This fact it is explained by the presence of long-range (fractal) correlation along with distinct classes of non-linear interactions [3]. The feature set described in our previous work [4] is a combination of time-frequency domain, perceptual and fractal analysis.…”